Andrea Gsur

Colorectal cancer (CRC) represents the third leading cause of cancer related deaths worldwide and is influenced by multiple risk factors. Both exposure to microbiota as well as adaptive immune responses have been implicated in CRC development and suggested as candidate biomarkers. However, the extent to which CRC and adenomas associate with systemic immune signatures against microbes remains unclear. Here, we apply Phage-display ImmunoPrecipitation Sequencing (PhIP-Seq) on 321 individuals of an Austrian CORSA cohort (Colorectal Cancer Study of Austria) including controls, adenomas, and CRC patients. Although antibody repertoires showed global similarity across the different disease status and limited predictive performance, prevalence-based analyses revealed specific signals, such as Helicobacter pylori CagY as candidate marker of CRC and viral antigens as candidate for non-cancer stages. Taken together, this large-scale analysis of antibody reactivity in CRC highlights the potential of serological markers to reflect disease-associated immune signatures.

Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions. EAGLE combines task-agnostic tile selection with detailed feature extraction and is benchmarked against leading slide- and tile-level foundation models across 43 tasks from nine cancer types spanning morphology, biomarker prediction, treatment response and prognosis. EAGLE outperforms patch aggregation methods by up to 23% and achieves the highest overall classification performance. It processes one slide in 2.27 s, reducing computational time by more than 99% compared with existing models. This efficiency supports rapid and auditable workflows by enabling review of the exact tiles used for each prediction and reducing dependence on high-performance computing. By reliably identifying informative regions and minimizing artifacts, EAGLE provides robust and auditable outputs, supported by systematic negative controls and attention concentration analyses. Its unified embedding enables rapid slide search, integration into multi-omics pipelines and emerging clinical foundation models.

Supplementary Table S9 shows the predictive value of endoscopy history in GERA.

This figure depicts the flowchart of the two-step interaction test.

Supplementary Table S1 shows the descriptive characteristics of study populations in GECCO and CORECT.

This file includes the association parameters (OR [95% CI]) for the identified SNPs with suggestive interaction term for smoking status by genotypes.

Supplementary Table S3 shows the comparison on characteristics between GECCO/CORECT study and GERA Europeans-ancestry participants

Background:Mechanistic data indicate the benefit of short-chain fatty acids (SCFA) produced by gut microbial fermentation of fiber on colorectal cancer, but direct epidemiologic evidence is limited. A recent study identified SNPs for two SCFA traits (fecal propionate and butyrate-producing microbiome pathway PWY-5022) in Europeans and showed metabolic benefits.Methods:We conducted a two-sample Mendelian randomization analysis of the genetic instruments for the two SCFA traits (three SNPs for fecal propionate and nine for PWY-5022) in relation to colorectal cancer risk in three large European genetic consortia of 58,131 colorectal cancer cases and 67,347 controls. We estimated the risk of overall colorectal cancer and conducted subgroup analyses by sex, age, and anatomic subsites of colorectal cancer.Results:We did not observe strong evidence for an association of the genetic predictors for fecal propionate levels and the abundance of PWY-5022 with the risk of overall colorectal cancer, colorectal cancer by sex, or early-onset colorectal cancer (diagnosed at <50 years), with no evidence of heterogeneity or pleiotropy. When assessed by tumor subsites, we found weak evidence for an association between PWY-5022 and risk of rectal cancer (OR per 1-SD, 0.95; 95% confidence intervals, 0.91–0.99; P = 0.03) but it did not surpass multiple testing of subgroup analysis.Conclusions:Genetic instruments for fecal propionate levels and the abundance of PWY-5022 were not associated with colorectal cancer risk.Impact:Fecal propionate and PWY-5022 may not have a substantial influence on colorectal cancer risk. Future research is warranted to comprehensively investigate the effects of SCFA-producing bacteria and SCFAs on colorectal cancer risk.

Supplementary Figure from Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region

Supplementary Figure S5 shows the age-stratified calibration on 5- and 10-year absolute risks based on the PRS-enhanced model stratified by deciles of predicted risk in GERA European ancestry participants.